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@agentled/cli

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CLI for Agentled — manage workflows, apps, and knowledge from the command line. Zero context-window cost for AI agents.

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{ "name": "AI with Runtime Tools", "goal": "AI step that can call web_search + workspace_memory at runtime (aiActionWithTools shape).", "description": "Starting shape for an AI step that needs to pull fresh web context and recall/store workspace memory. Replace the prompt and response structure for your task.", "status": "draft", "context": { "executionInputConfig": { "title": "Research + Recall", "description": "Provide a topic or entity to research.", "runCTALabel": "Run", "fields": [ { "name": "topic", "label": "Topic or entity", "type": "text", "required": true } ] } }, "steps": [ { "id": "start", "type": "trigger", "name": "Manual Start", "pipelineStepStartConditions": { "trigger": { "type": "manual" } }, "next": { "stepId": "analyze" } }, { "id": "analyze", "type": "aiActionWithTools", "name": "Analyze with Tools", "tools": [ { "type": "builtin", "name": "web_search", "builtinType": "web_search" }, { "type": "builtin", "name": "workspace_memory", "builtinType": "workspace_memory" } ], "pipelineStepPrompt": { "template": "Research the topic: {{input.topic}}.\n\nUse `web_search` for fresh external context. Use `workspace_memory` to recall what we already know about {{input.topic}} (call action \"search\" with a relevant query) and to `store` any durable fact you learn (category=fact, confidence 70-100).\n\nReturn a concise structured summary.", "responseStructure": { "summary": "string — 3-5 sentences synthesising research + prior memory", "sources": "array of strings — URLs cited from web_search", "stored_memories": "array of strings — keys of new memories written (if any)" } }, "creditCost": 10, "next": { "stepId": "done" } }, { "id": "done", "type": "milestone", "name": "Done" } ] }